Controlling a material processing tool and performance data

ABSTRACT

According to an embodiment of the present invention, a material processing systeme ( 1 ) including a process tool ( 10 ) and a process performance control system ( 100 ). The process performance control system ( 100 ) includes a process performance controller ( 55 ) coupled to the process tool ( 10 ), where the process performance controller ( 55 ) includes a process performance prediction model ( 110 ), a process recipe correction filter ( 120 ), a process controller ( 130 ), and process performance model correction algorithm ( 150 ). The process performance prediction model ( 110 ) is configured to receive tool data from a plurality of sensors coupled to process tool ( 10 ) and to predict process performance data. The process recipe correction filter ( 120 ) is coupled to the process performance prediction model ( 110 ) and configured to receive predicted process performance data and generate a process recipe correction for run-to-run process control. The process controller ( 130 ) is coupled to the process recipe correction filter ( 120 ) and is configured to update a process recipe according to the process recipe correction.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to previously filed U.S.application Ser. No. 60/391,966, filed on Jun. 28, 2002. Thisapplication is related to U.S. application Ser. No. 60/391,965, filed onJun. 28, 2002. The entire contents of these applications areincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to material processing and moreparticularly to a process performance control system and method thereoffor controlling a process in a material processing system.

BACKGROUND OF THE INVENTION

One area of material processing in the semiconductor industry whichpresents formidable challenges is, for example, the manufacture ofintegrated circuits (ICs). Demands for increasing the speed of ICs ingeneral, and memory devices in particular, force semiconductormanufacturers to make devices smaller and smaller on the substratesurface. Moreover, in order to reduce fabrication costs, it is necessaryto reduce the number of steps (e.g., etch steps, deposition steps, etc.)required to produce an IC structure and, hence, reduce the overallcomplexity of the IC structure and the fabrication methods thereof.These demands are further exacerbated by both the reduction in featuresize and the increase of substrate size (i.e., 200 mm to 300 mm andgreater), which places greater emphasis on critical dimensions (CD),process rate and process uniformity to maximize the yield of superiordevices. through inter-level dielectric layers. Usually, an etch stoplayer is placed under a dielectric layer in order to protect theunderlying layers (devices) from being damaged during over-etching. Anetch stop layer generally includes a material that when exposed to thechemistry utilized for etching the dielectric layer has an etch rateless than the dielectric layer etch rate (i.e., the etch chemistry has ahigh etch selectivity to the dielectric layer relative to the etch stoplayer). Furthermore, the etch stop layer provides a barrier forpermitting an over-etch step to assure that all features on thesubstrate are etched to the same depth.

However, the etch stop layer complicates the process integration,increases manufacturing cost and decreases device performance. Withoutan etch stop layer, etch depth can vary depending on etch rate (ER)since fixed-time recipes are used. Since, for example, the etch tool issubject to equipment disturbance, the etch rate can change significantlyover maintenance cycles. In order to maintain a constant etch rate,frequent tool qualification and maintenance procedures are required.Therefore, in-situ estimation of the etch rate can determine whether theprocess chamber is in a normal condition and can provide information tocontrol the etch time so that the etch depth is on target.

SUMMARY OF THE INVENTION

The present invention provides for a material processing systemcomprising a process tool and a process performance control system. Theprocess performance control system comprises a process performancecontroller coupled to the process tool, where the process performancecontroller comprises a process performance prediction model configuredto receive tool data from a plurality of sensors and configured topredict process performance data. The process performance control systemfurther comprises a process recipe correction filter coupled to theprocess performance prediction model, configured to receive predictedprocess performance data and configured to generate a process recipecorrection, and a process controller coupled to the process recipecorrection filter, configured to receive the process recipe correctionand update the process recipe using the process recipe correction.

The present invention advantageously provides a process performancecontrol system that further comprises a metrology tool coupled to theprocess tool and the process performance controller, configured toreceive substrates processed within the process tool and configured tomeasure process performance data. The process performance controllerfurther comprises a process performance model correction algorithmconfigured to receive measured process performance data and coupled tothe process performance prediction model in order to provide anadjustment of the process performance prediction model.

The present invention advantageously provides a method for controlling aprocess tool of a material processing system. The method comprises thesteps of executing a first process in the process tool, recording tooldata for the first process, where the tool data comprises a plurality oftool data parameters, and predicting process performance data from thetool data for the first process using a process performance predictionmodel, where the process performance data comprises one or more processperformance data parameters. The method further comprises the steps ofdetermining a process recipe correction from the predicted processperformance data using a process recipe correction filter, updating theprocess recipe by incorporating the process recipe correction using aprocess controller coupled to the process tool, and executing a secondprocess in the process tool using the updated process recipe.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other advantages of the invention will become more apparentand more readily appreciated from the following detailed description ofthe exemplary embodiments of the invention taken in conjunction with theaccompanying drawings, where:

FIG. 1 shows a material processing system according to a preferredembodiment of the present invention;

FIG. 2 shows a material processing system according to one embodiment ofthe present invention;

FIG. 3 shows a material processing system according to anotherembodiment of the present invention;

FIG. 4 shows a material processing system according to a furtherembodiment of the present invention;

FIG. 5 shows a material processing system according to an additionalembodiment of the present invention;

FIG. 6 presents a schematic representation of some of the inputs andoutputs for a partial least squares (PLS) analysis model;

FIG. 7 presents an exemplary output of statistics from a PLS analysismodel;

FIG. 8 presents an exemplary graph of work set loadings w*c(1) versusw*c(2);

FIG. 9 presents an exemplary graph of work set scores t(1) versus u(1);

FIG. 10 presents an exemplary set of coefficients for a mean trench etchdepth model;

FIG. 11 presents an exemplary set of coefficients for a trench etchdepth range model;

FIG. 12 presents an exemplary distribution for a set of variableimportance in the projection (VIP) data;

FIG. 13 shows exemplary criteria for refining the tool data using VIPdata;

FIG. 14 presents an exemplary comparison between the observed meantrench etch depth and the predicted mean trench etch depth;

FIG. 15 presents an exemplary comparison between the observed trenchetch depth range and the predicted trench etch depth range;

FIG. 16 presents a flow diagram of a method of constructing a processperformance prediction model according to an embodiment of the presentinvention;

FIG. 17 presents a flow diagram of a method of fault detection using aprocess performance prediction model according to an embodiment of thepresent invention;

FIG. 18 presents an exemplary graph of the root mean square error (RMSE)as a function of the exponentially weighted moving average (EMWA) filtercoefficient;

FIG. 19 presents an exemplary graph of the measured trench etch rate andthe predicted trench etch rate as a function of substrate number for afilter coefficient of 0.5;

FIG. 20 presents an exemplary graph of the measured trench etch rate andthe predicted trench etch rate as a function of substrate number usingperiodic updates of the process performance prediction model; and

FIG. 21 presents a flow diagram of a method of controlling a processrecipe for a material processing system according to an embodiment ofthe present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

According to an embodiment of the present invention, a materialprocessing system 1 is depicted in FIG. 1 including a process tool 10and a process performance control system 100. The process performancecontrol system 100 includes a process performance controller 55 coupledto the process tool 10, where the process performance controller 55includes a process performance prediction model 110, a process recipecorrection filter 120, a process controller 130, and a processperformance model correction algorithm 150. The process performanceprediction model 110 is configured to receive tool data from a pluralityof sensors coupled to process tool 10 and to predict process performancedata. The process recipe correction filter 120 is coupled to the processperformance prediction model 110 and configured to receive predictedprocess performance data and generate a process recipe correction forrun-to-run process control. The process controller 130 is coupled to theprocess recipe correction filter 120 and is configured to update aprocess recipe according to the process recipe correction.

In addition, the process performance control system 100 can furtherinclude a metrology tool 140, and the process performance controller 55can further include a process performance model correction algorithm150. Metrology tool 140 can be coupled to the process tool 10 and to theprocess performance controller 55, and metrology tool 140 can beconfigured to receive substrates processed within the process tool 10and to measure process performance data. The process performancecorrection algorithm 150 can be configured to receive measured processperformance data from the metrology tool 140, and the processperformance correction algorithm 150 can be coupled to processperformance prediction model 110 in order to provide an adjustment ofthe process performance prediction model 110.

In the illustrated embodiment depicted in FIG. 2, material processingsystem 1 can utilize a plasma for material processing. For example, thematerial processing system 1 includes an etch chamber acting as aprocess tool 10 a. Alternately, material processing system 1 can includeother process tools 10 such as a photoresist coating chamber such as aphotoresist spin coating system; a photoresist patterning chamber suchas an ultraviolet (UV) lithography system; a dielectric coating chambersuch as a spin-on-glass (SOG) or spin-on-dielectric (SOD) system; adeposition chamber such as a chemical vapor deposition (CVD) system or aphysical vapor deposition (PVD) system; a rapid thermal processing (TP)chamber such as a RTP system for thermal annealing; or a batch diffusionfurnace.

As shown in FIG. 2, when material processing system 1 includes an etchor deposition chamber as a process tool 10, the system often furtherincludes substrate holder 20, upon which a substrate 25 to be processedis affixed, gas injection system 40, and vacuum pumping system 58.Substrate 25 can be, for example, a semiconductor substrate, a wafer, ora liquid crystal display (LCD). Process tool 10 can be, for example,configured to facilitate the generation of plasma in processing region45 adjacent a surface of substrate 25, where plasma is formed viacollisions between heated electrons and an ionizable gas. For example,an ionizable gas or mixture of gases can be introduced via gas injectionsystem 40 and the process pressure can be adjusted using vacuum pumpingsystem 58. Desirably, plasma is utilized to create materials specific toa predetermined materials process, and to aid either the deposition ofmaterial to substrate 25 or the removal of material from the exposedsurfaces of substrate 25.

For example, the substrate 25 can be affixed to the substrate holder 20via an electrostatic clamping system 28. Furthermore, substrate holder20 can further include a cooling system including a re-circulatingcoolant flow that receives heat from substrate holder 20 and transfersheat to a heat exchanger system (not shown), or when heating, transfersheat from the heat exchanger system. Moreover, gas can be delivered tothe back-side of the substrate via a backside gas system 26 to improvethe gas-gap thermal conductance between substrate 25 and substrateholder 20. Such a system can be utilized when temperature control of thesubstrate is required at elevated or reduced temperatures. For example,temperature control of the substrate can be useful at temperatures inexcess of the steady-state temperature achieved due to a balance of theheat flux delivered to the substrate 25 from the plasma and the heatflux removed from substrate 25 by conduction to the substrate holder 20.In other embodiments, heating elements, such as resistive heatingelements, or thermoelectric heaters/coolers can be included.

In the exemplary embodiment shown in FIG. 2, substrate holder 20 canfurther serve as an electrode through which radio frequency (RF) poweris coupled to plasma in processing region 45. For example, substrateholder 20 can be electrically biased at an RF voltage via thetransmission of RF power from RF generator 30 through impedance matchnetwork 32 to substrate holder 20. The RF bias can serve to heatelectrons to form and maintain plasma. In this configuration, the systemcan operate as a reactive ion etch (RIE) reactor, where the chamber andupper gas injection electrode serve as ground surfaces. A typicalfrequency for the RF bias can range from 1 MHz to 100 MHz and ispreferably 13.56 MHz.

Alternately, RF power can be applied to the substrate holder electrodeat multiple frequencies. Furthermore, impedance match network 32 canserve to maximize the transfer of RF power to plasma in processingchamber 10 by minimizing the reflected power. Various match networktopologies (e.g., L-type, π-type, T-type, etc.) and automatic controlmethods can be utilized.

With continuing reference to FIG. 2, process gas can be introduced toprocessing region 45 through gas injection system 40. Process gas can,for example, include a mixture of gases such as argon, CF₄ and O₂, orargon, C₄F₈ and O₂ for oxide etch applications, or other chemistriessuch as O₂/CO/Ar/C₄F₈, O₂/CO/AR/C₅F₈, O₂/CO/Ar/C₄F₆, O₂/Ar/C₄F₆, N₂/H₂.Gas injection system 40 can include a showerhead, where process gas issupplied from a gas delivery system (not shown) to the processing region45 through a gas injection plenum (not shown), a series of baffle plates(not shown) and a multi-orifice showerhead gas injection plate (notshown).

Vacuum pump system 58 can, for example, include a turbo-molecular vacuumpump (TMP) capable of a pumping speed up to 5000 liters per second (andgreater) and a gate valve for throttling the chamber pressure. Inrelated plasma processing devices utilized for dry plasma etch, a 1000to 3000 liter per second TMP is generally employed. TMPs are useful forlow pressure processing, typically less than 50 mTorr. At higherpressures, the TMP pumping speed falls off dramatically. For highpressure processing (i.e., greater than 100 mTorr), a mechanical boosterpump and dry roughing pump can be used. Furthermore, a device formonitoring chamber pressure (not shown) is coupled to the processchamber 16. The pressure measuring device can be, for example, a Type628B Baratron absolute capacitance manometer commercially available fromMKS Instruments, Inc. (Andover, Mass.).

As depicted in FIG. 1, the process performance control system 100includes a process performance controller 55 coupled to the process tool10 and configured to receive tool data from a plurality of sensors. Theplurality of sensors can include both sensors that are intrinsic to theprocess tool 10 and sensors extrinsic to the process tool 10. Sensorsintrinsic to the process tool 10 can include those sensors pertaining tothe functionality of the process tool 10 such as the measurement of theHelium backside gas pressure, Helium backside flow, electrostaticclamping (ESC) voltage, ESC current, substrate holder 20 temperature (orlower electrode (LEL) temperature), coolant temperature, upper electrode(UEL) temperature, forward RF power, reflected RF power, RF self-inducedDC bias, RF peak-to-peak voltage, chamber wall temperature, process gasflow rates, process gas partial pressures, chamber pressure, capacitorsettings (i.e., C₁ and C₂ positions), a focus ring thickness, RF hours,focus ring RF hours, and any statistic thereof. Alternatively, sensorsextrinsic to process tool 10 can include those not directly related tothe functionality of process tool 10 such as, for example, a lightdetection device 34 for monitoring the light emitted from the plasma inprocessing region 45 as shown in FIG. 2, or an electrical measurementdevice 36 for monitoring the electrical system of process tool 10 asshown in FIG. 2.

For example, light detection device 34 can include a detector such as a(silicon) photodiode or a photomultiplier tube (PMT) for measuring thetotal light intensity emitted from the plasma. It can further include anoptical filter such as a narrow-band interference filter. In analternate embodiment, light detection device 34 can include a line CCD(charge coupled device) or CID (charge injection device) array and alight dispersing device such as a grating or a prism. Additionally,light detection device 34 can include a monochromator (e.g.,grating/detector system) for measuring light at a given wavelength, or aspectrometer (e.g., with a rotating grating) for measuring the lightspectrum such as, for example, the device described in U.S. Pat. No.5,888,337.

For example, the light detection device 34 can include a high resolutionOES sensor from Peak Sensor Systems. Such an OES sensor has a broadspectrum that spans the ultraviolet (UV), visible (VIS) and nearinfrared (NIR) light spectrums. The resolution is approximately 1.4Angstroms, that is, the sensor is capable of collecting 5550 wavelengthsfrom 240 to 1000 nm. The sensor is equipped with high sensitivityminiature fiber optic UV-VIS-NIR spectrometers which are, in turn,integrated with 2048 pixel linear CCD arrays.

The spectrometers receive light transmitted through single and bundledoptical fibers, where the light output from the optical fibers isdispersed across the line CCD array using a fixed grating. Similar tothe configuration described above, light emitting through an opticalvacuum window is focused onto the input end of the optical fibers via aconvex spherical lens. Three spectrometers, each specifically tuned fora given spectral range (UV, VIS and NIR), form a sensor for a processchamber. Each spectrometer includes an independent A/D converter. Andlastly, depending upon the sensor utilization, a full emission spectrumcan be recorded every 0.1 to 1.0 seconds.

The electrical measurement device 36 can include, for example, a currentand/or voltage probe for monitoring an electrical property, such asvoltage, current, impedance and phase, of the electrical systemincluding the processing region 45, a power meter, or spectrum analyzer.For example, plasma processing systems often employ RF power to formplasma, in which case, an RF transmission line, such as, for instance, acoaxial cable or structure, is employed to couple RE energy to theplasma through an electrical coupling element (i.e., inductive coil,electrode, etc.). Electrical measurements using, for example, acurrent-voltage probe, can be exercised anywhere within the electrical(RF) circuit, such as within an RE transmission line. Furthermore, themeasurement of an electrical signal, such as a time trace of voltage orcurrent, permits the transformation of the signal into frequency spaceusing discrete Fourier series representation (assuming a periodicsignal). Thereafter, the Fourier spectrum (or for a time varying signal,the frequency spectrum) can be monitored and analyzed to characterizethe state of material processing system 1. A voltage-current probe canbe, for example, a device as described in detail in pending U.S.application Ser. No. 60/259,862 filed on Jan. 8, 2001, and U.S. Pat. No.5,467,013, each of which is incorporated herein by reference in itsentirety.

In alternate embodiments, electrical measurement device 36 can comprisea broadband RF antenna useful for measuring a radiated RE field externalto material processing system 1. A commercially available broadband RFantenna is a broadband antenna such as Antenna Research Model RAM-220(0.1 MHz to 300 MHz).

In general, the plurality of sensors can include any number of sensors,intrinsic and extrinsic, and can be coupled to process tool 10 toprovide tool data to the process performance controller 55 of theprocess performance control system 100.

As described above, the process performance control system 100 includesprocess performance controller 55. The process performance controller 55can include a microprocessor, memory, and a digital I/O port(potentially including D/A and/or A/D converters) capable of generatingcontrol voltages sufficient to communicate and activate inputs tomaterial processing system 1 as well as monitor outputs from materialprocessing system 1. Moreover, process performance controller 55 iscoupled to and exchanges information with RF generator 30, impedancematch network 32, gas injection system 40, vacuum pump system 58,backside gas delivery system 26, electrostatic clamping system 28, lightdetection device 34, and electrical measurement device 36. A programstored in the memory is utilized to activate the inputs to theaforementioned components of a material processing system 1 according toa stored process recipe. One example of process performance controller55 is a DELL PRECISION WORKSTATION 530™, available from DellCorporation, Austin, Tex. Alternately, process performance controller 55can comprise a Digital Signal Processor (DSP).

As shown in FIG. 3, material processing system 1 can include magneticfield system 60. For example, magnetic field system 60 can include astationary or either a mechanically or electrically rotating DC magneticfield in order to potentially increase plasma density and/or improvematerial processing uniformity. Moreover, process performance controller55 can be coupled to magnetic field system 60 in order to regulate thefield strength or the speed of rotation.

As shown in FIG. 4, the material processing system of FIG. 1 can includean upper electrode 70. For example, RF power can be coupled from RFgenerator 72 through impedance match network 74 to upper electrode 70. Afrequency for the application of RF power to the upper electrodepreferably ranges from 10 MHz to 200 MHz and is preferably 60 MHz.Additionally, a frequency for the application of power to the lowerelectrode preferably ranges from 0.1 MHz to 30 MHz and is preferably 2MHz. Moreover, process performance controller 55 can be coupled to RFgenerator 72 and impedance match network 74 in order to control theapplication of RF power to upper electrode 70. The design andimplementation of an upper electrode is well known to those skilled inthe art.

As shown in FIG. 5, the material processing system of FIG. 1 can includeinductive coil 80. For example, RF power can be coupled from RFgenerator 82 through impedance match network 84 to inductive coil 80,and RF power can be inductively coupled from inductive coil 80 throughdielectric window (not shown) to plasma processing region 45. Afrequency for the application of RF power to the inductive coil 80preferably ranges from 10 MHz to 100 MHz and is preferably 13.56 MHz.Similarly, a frequency for the application of power to the chuckelectrode preferably ranges from 0.1 MHz to 30 MHz and is preferably13.56 MHz. In addition, a slotted Faraday shield (not shown) can beemployed to reduce capacitive coupling between the inductive coil 80 andplasma. Moreover, process performance controller 55 can be coupled to RFgenerator 82 and impedance match network 84 in order to control theapplication of power to inductive coil 80. In an alternate embodiment,inductive coil 80 can be a “spiral” coil or “pancake” coil incommunication with the plasma processing region 45 from above as in atransformer coupled plasma (TCP) reactor.

Alternately, the plasma can be formed using electron cyclotron resonance(ECR). In yet another embodiment, the plasma is formed from thelaunching of a Helicon wave. In yet another embodiment, the plasma isformed from a propagating surface wave.

As discussed above, the process performance prediction model 100establishes a relationship between tool data and process performancedata and, therefore, it enables the prediction of process performancedata for a given observation of tool data. The following describes themethod of constructing the process performance prediction model 110.

Table 1 presents an exemplary set of tool data, to be correlated withprocess performance data, including sixty-one tool data parameters.

TABLE 1 Exemplary tool data. PARAMETER DESCRIPTION PARAMETER DESCRIPTIONAPC Adaptive pressure control valve setting RF_FORWARD-S Forward RFpower, Standard deviation HE_C_PRESS Helium backside pressure (center),Average C2_POSITION Capacitor no.2 position, Average AR_FLOW Argon gasflow rate, Average ESC_CURRENT Electrostatic clamp current, AveragePRESSURE Chamber pressure, Average LOWER_TEMP LEL temperature AverageUPPER_TEMP UEL temperature, Average RF_REFLECT Reflected RF power,Average VIP_Z Current-voltage probe impedance, Average VIP_PHASECurrent-voltage probe phase, Average HE_C_FLOW-S Helium backside flow(center) Standard deviation APC-S Adaptive pressure control valvesetting, Standard Deviation ESC_VOLTAGE-S Electrostatic clamp voltage,Standard deviation HE_C_PRES-S Helium backside pressure (center),Standard deviation MAGNITUDE-S Match network, control signal, magnitude,AR_FLOW-S Argon gas flow rate, Standard deviation Standard deviationRF_VDC-S DC voltage, RF system, Standard deviation PRESSURE-S Chamberpressure, Standard deviation VIP_RF_ON-S Current voltage probe on/offstatus, UPPER_TEMP-S UEL temperature, Standard deviation Standarddeviation C1_POSITION Capacitor no. 1 position, Average VIP_Z-SCurrent-voltage probe impedance, Standard deviation HE_E_PRES Helliumbackside pressure (edge) Average HE_C FLOW Helium backside flow(center), Average C5F8_FLOW C5F8 gas flow rate, Average ESC_VOLTAGEElectrostatic clamp voltage, Average RF_FORWARD Forward RF power,Average MAGNITUDE Match network control signal, magnitude, Average VIP_ICurrent-voltage probe current, Average RF_VDC DC voltage, RF system,Average WALL_TEMP Chamber wall temperature Average VIP_RF_ON Currentvoltage probe on/off status, Average HE_E_FLOW-S Helium backside flow(edge), Standard deviation C1_POSITION-S Capacitor no.1 position,Standard deviation O2_FLOW-S Oxygen gas flow rate, Standard deviationHE_E_PRES-S Helium backside pressure (edge), Standard deviation PHASE-SMatch network, control signal, phase, C5F8_FLOW-S C5F8 gas flow rate,Standard deviation Standard deviation RF_VPP-S RF voltage peak-to-peakStandard deviation VIP_I-S Current-voltage probe current, Standarddeviation VIP_V-S Current-voltage probe voltage Standard deviationWALL_TEMP-S Chamber wall temperature, Standard deviation HE_E_FLOWHelium backside flow (edge), Average VIP_PHASE-S Current-voltage probephase, Standard deviation O2_FLOW Oxygen gas flow rate, Average RF_HR RFhours PHASE Match network control signal, phase, Average SLOT_ID Waferslot Index RF_VPP RF voltage peak-to-peak, Average RF_HRxFR_THK RF hours(X) Focus ring thickness VIP_V Current-voltage probe voltage, AverageRF_HRxFR_RFHR RF hours (X) Focus ring RF hours C2_POSITION-S Capacitorno. 2 position, Standard deviation FR_THK Focus ring thicknessESC_CURRENT-S Electrostatic clamp current, Standard deviation FR_RFHRFocus ring RF hours LOWER_TEMP-S LEL temperature, Standard deviationFR_THKxFR_RFHF Focus ring thickness (X) Focus ring RF hours RF_REFLECT-SReflected RF power, Standard deviation

Moreover, an exemplary set of process performance data pertaining totrench etching as part of a damascene process can include a mean trenchetch depth and a trench etch depth range. The mean trench etch depthcan, for example, include a spatial average of the trench etch depth ata plurality of locations on a substrate. The trench etch depth rangecan, for example, include a minimum-maximum range, a variance, astandard deviation, or a root mean square (rms) of the data scatterabout the mean value for the etch depth.

The measurement of the trench etch depth and trench etch depth range canbe performed directly using a scanning electron microscope (SEM) to viewSEM micrographs from cleaved substrates, or indirectly using advanced,in-situ technology such as, for example, DUV spectroscopic ellipsometry(e.g., see “Specular spectroscopic scatterometry”, IEEE Transactions onSemiconductor Manufacturing, Vol. 14, No. 2, May 2001, which isincorporated herein by reference in its entirey). A commerciallyavailable product featuring optical digital profilometry (ODP) is thatsold and distributed by Timbre Technologies, Inc., A TEL Company (5341Randall Place, Fremont, Calif. 94538) coupled with the hardware fromTherma-Wave, Inc. (1250 Reliance Way, Fremont, Calif. 94539).

Each set of data, including both tool data and corresponding processperformance data, includes an observation set, where either a singleobservation can be made per substrate or a plurality of observations canbe performed per substrate. Each observation in an observation set,including both tool data and process performance data, can include ann^(th) order statistic (e.g., time average, rms of time trace, skewnessof time trace, etc.). For example, each observation set can correspondto a substrate processed, wherein each tool data parameter is sampledduring the length of the process, trimmed (i.e., data at the start andend of the sampled data is trimmed to remove start/end transients), andaveraged.

Given a plurality of observations sets, a relationship can be determinedbetween the tool data in the plurality of observation sets and theprocess performance data in the plurality of observation sets usingmultivariate analysis (MVA). One exemplary MVA technique for determiningsuch a relationship is partial least squares (PLS) modeling.

Using PLS, observation sets of tool data are received from a pluralityof sensors. For each observation set, tool data can be stored as a rowin a matrix X and process performance data can be stored as a row inmatrix Y. Hence, once the matrix X is assembled, each row represents adifferent observation and each column represents a different tool dataparameter (from Table 1), and, once the matrix Y is assembled, each rowrepresents a different observation and each column represents adifferent process performance parameter. Hence, using the set ofparameters in Table 1, matrix X is a rectangular matrix of dimensions Mby sixty-one, where M is the number of observation sets. Similarly,matrix Y is a rectangular matrix of dimensions M by 2. More generally,matrix X can be an m by n matrix, and matrix Y can be an m by p matrix.Once all of the data is stored in the matrices, the data can bemean-centered and/or normalized, if desired. The process ofmean-centering the data stored in a matrix column involves computing amean value of the column elements and subtracting the mean value fromeach element. Moreover, the data residing in a column of the matrix canbe normalized by the standard deviation of the data in the column.

In the following discussion, a set of tool data and process performancedata is utilized from forty-five substrates in order to present themethod by which tool data are optimized and a model is established forrelating the tool data and the process performance data (i.e., M=45 inthe above discussion.) The forty-five process runs (substrates) includesthree sets of substrates processed in an etch chamber, where each set ofsubstrates is preceded by a chamber wet clean. The tool data included inthe PLS analysis model is listed in Table 1 and the process performancedata includes the mean trench etch depth and the trench etch depthrange.

In the PLS analysis, a set of loading (or correlation) coefficients canbe defined which relate the tool data ( X) to the process performancedata ( Y). In general, for multivariate analysis, the relationshipbetween the tool data and the process performance data can be expressedas follows:XB= Y,  (1)where X represents the m by n matrix described above, B represents an nby p (p<n) loading (or correlation) matrix and Y represents the m by pmatrix described above.

Once the data matrices X and Y are assembled, a relationship designed tobest approximate the X and Y spaces and to maximize the correlationbetween X and Y is established using PLS analysis.

In the PLS analysis model, the matrices X and Y are decomposed asfollows:X= TP ^(T) +Ē;  (2a)Y= UC ^(T) + F;  (2b)andŪ= T+ H;  (2c)where T is a matrix of scores that summarizes the X variables, P is amatrix of loadings for matrix X, Ū is a matrix of scores that summarizesthe Y variables, C is a matrix of weights expressing the correlationbetween Y and T ( X), and Ē, F and H are ma residuals. Furthermore, inthe PLS analysis model, there are additional loadings W called weightsthat correlate Ū and X, and are used to calculate T. In summary, the PLSanalysis geometrically corresponds to fitting a line, plane or hyperplane to both the X and Y data represented as points in amultidimensional space, with the objective of well approximating theoriginal data tables X and Y, and maximizing the covariance between theobservation positions on the hyper planes.

FIG. 6 provides a schematic representation of the data inputs, X and Y,to the PLS analysis and the corresponding outputs T, P, Ū, C, W, W, F, Hand variable importance in the projection (VIP). An example of acommercially available software which supports PLS analysis modeling isSIMCA-P 8.0. For further details on this software see the User's Manual(User Guide to SIMCA-P 8.0: A new standard in multivariate, dataanalysis, Umetrics AB, Version 8.0, September 1999).

In general, SIMCA-P outputs other important information regarding thedescriptive power of the model (i.e., the quality of the correlationobtained between X and Y), and the predictive power of the model. Forexample, SIMCA-P iteratively computes one PLS component at a time, thatis one vector each of X-scores T, Y-scores Ū, weights W and C, andloadings P. The PLS components are calculated in descending order ofimportance. After each PLS component, SIMCA-P can display the following:the fraction of the sum of squares (SS) of all Y's and X's explained bythe current component (R²X, R²Y); the fraction of variance of all theY's and X's explained by the current component R²Xadj, R²Yadj); thecumulative SS of all the Y's and X's explained by all extractedcomponents (R²X(cum), R²Y(cum)); and the cumulative variance of all theY's and X's explained by all extracted components (R²Xadj(cum),R²Yadj(cum)).

Furthermore, for every active variable, the fraction of SS (R²V) orvariance (R²Vadj) explained can be displayed. This value is computed forthe current component as well as cumulatively for all PLS components.For response variables Y, this value corresponds to R² (the multiplecorrelation coefficient), the “goodness” of the fit. For example,utilizing the data above, FIG. 7 presents this value for Y (R²VY(cum))for each process performance parameter, namely, the mean etch depth andthe etch depth range. By inspection of FIG. 7, the “goodness” of eachfit exceeds ninety-seven percent when using the first four PLScomponents.

In general, additional criterion used to determine the modeldimensionality (number of significant PLS components), is crossvalidation. With cross validation, observations are kept out of themodel development, then the response values ( Y) for the kept outobservations are predicted by the model, and compared with the actualvalues. This procedure is repeated several times until every observationhas been kept out once and only once. The prediction error sum ofsquares (PRESS) is the squared differences between observed Y andpredicted values when the observations were kept out. For everydimension, the overall PRESS/SS is computed, where SS is the residualsum of squares of the previous dimension, and also (PRESS/SS)_(m) foreach Y variable (m). These values are good measures of the predictivepower of the model. For example, SIMCA-P can present this information asfollows: the fraction of the total variation of the Y's that can bepredicted by a component (Q²=(1.0-PRESS/SS)); the fraction of thevariation of a variable Y_(m) that can be predicted by a component(Q²V=(1.0-PRESS/SS)_(m)); the cumulative Q² for the extracted components(Q² _(cum)=II (1.0-PRESS/SS)_(a)); and the cumulative Q²V of a variable(Q²V_(cum)=II (1.0-PRESS/SS)_(ka)). FIG. 7 further presents thepredictive power (Q²V_(cum)) for each process performance parameter,namely, the mean etch depth and the etch depth range. By inspection ofFIG. 7, the predictive power of each fit exceeds ninety-one percent whenusing the first four PLS components.

FIG. 8 presents the work set loadings, w*c(1) versus w*c(2), for thetool data and process performance data described above. The plot showsboth the X-weights (w or w*) and Y-weights (c), and thereby thecorrelation structure between X and Y. In addition, FIG. 8 shows how theX and Y variables combine in the projections, and how the X variablesrelate to the Y variables. For instance, two regions (upper right handcorner and lower left hand corner) indicate where a “strong” correlationexists between the tool data parameters and the process performanceparameters. Several tool data parameters from Table 1 are shown in FIG.8 as examples of how tool data parameters are correlated to processperformance. The oval in FIG. 8 defines a region where the remainingtool data parameters of Table 1 are grouped around the center point ofthe plot indicating that the parameters are not closely correlated toprocess performance.

FIG. 9 presents the work set scores, t(1) versus u(1) for the 45substrates. This plot displays the objects in the projected X(T) andY(U) space, and shows how well the Y space coordinate (u) correlateswith the X space coordinate (t).

FIGS. 10 and 11 present the coefficients assigned to each tool dataparameter for the mean etch depth model and the etch depth range model,respectively.

Once the PLS analysis is complete and the above output matrices havebeen computed, the influence on the Y matrix of every term or column inthe X matrix, namely, the VIP is determined. VIP is the sum over allmodel dimensions of the contributions variable influence (VIN). For agiven PLS dimension, (VIN)_(ij) ² is related to the squared PLS weight(w_(ij))² of that term. The accumulated (over all PLS dimensions) value,

$\begin{matrix}{{{VIP}_{j} = {\sum\limits_{i}({VIN})_{j}^{2}}},} & (3)\end{matrix}$is used for further analysis. Once the VIPs are computed for eachvariable in matrix X, they may be sorted and plotted in descending orderagainst the variable number. Those variables with the largest VIP willhave the greatest impact on the process performance data in matrix Y.

For example, FIG. 12 shows the VIP (for a four PLS component model) inmonotonically descending order (i.e., the tool data parameters fallingon the left hand side of the plot are the most significant parameters inthe model).

Using the VIP data of FIG. 12, the relative significance of a given tooldata parameter on the process performance data Y can be assessed, andthereby the data matrix X can be refined by reducing the variabledimension n of the original data matrix X. Exemplary criterion used todiscard the variables of minimal impact or little significance to theprocess performance data include: (1) discard those variables whose VIPfalls less than a pre-specified threshold (see FIG. 13); (2) discardthose variables associated with VIPs in the lowest 10^(th) percentile orwithin some other predetermined range (or, in other words, retain thosevariables associated with the largest VIP in the top 90^(th) percentile,however, note that the percentile threshold or range selected can bedifferent from the 90/10 embodiment described herein); and (3) thefirst, second or higher derivative of the VIP with respect to thevariable number may be used to select a value for the VIP, below whichor above which those variables are discarded (i.e., a maximum in thefirst or second derivative, or when the first derivative becomes lessthan a predetermined threshold slope).

Using any one of the above-mentioned criteria, those variables that haveminimal impact on the process performance data can be discarded. Thisdata reduction or refinement, in turn, reduces the column space of thedata matrix X from p (sixty-one in the above example) to q (e.g.,<sixty-one parameters), and forms a “new”, reduced or refined datamatrix X* of dimensions m by q (forty-five by<sixty-one). Once aninitial data reduction has taken place, those tool data parametersimportant for establishing a “good” model between the tool data and theprocess performance data can be stored. Thereafter, further refinementor reduction of the data matrix X* can be performed and/or the methodcan proceed with re-computing the output matrices from the PLS analysismodel using the reduced data matrix X* and determining the correlationmatrix B for establishing the relationship between the tool data and theprocess performance data.

At this point, the PLS model is repeated following the schematicpresented in FIG. 6, except now the reduced matrix X* is used as theinput to the PLS analysis. The output matrices are then recomputed. Asstated above, the VIPs can be studied following the descriptionassociated with FIG. 13 to further refine the data matrix X*, or thecorrelation matrix B may be evaluated from the output data using thefollowing relationship:B= W ( P ^(T) W )⁻¹ C ^(T).  (4)

Once the data matrix X* has been optimized, a final pass through the PLSanalysis is generally required to update or re-compute the outputmatrices necessary for computing the correlation matrix B. Hereinafter,the evaluation of equation (4) leads to a set of correlationcoefficients to be used for extracting the predicted process performancedata from the sampled tool data.

FIG. 14 presents the measured mean trench etch depth for the 45substrates versus the predicted mean trench etch depth, and FIG. 15presents the measured trench etch depth range for the 45 substratesversus the predicted trench etch depth range. A slope of unity indicatesa good agreement between the measured and predicted values.

Using FIG. 16, a method for constructing a process performanceprediction model is described. Procedure 550 begins with step 510 wherethe matrix X is assembled from the observed tool data. As describedabove, each column represents a different tool data parameter and eachrow represents an observation. Similarly, in step 520, the matrix Y isassembled using observed process performance data. Again, each columnrepresents a different process performance data parameter and each rowrepresents an observation. In step 530, matrices X and Y are input intothe PLS analysis model to compute the above described output data (e.g.,loading data, weighting data, scores data, VIP data, etc.; see FIGS. 6through 12). In step 540, the PLS output statistics are checked todetermine if the PLS fitting power and/or predictive power areacceptable. In step 550, the VIP data are plotted and analyzed indescending order as in FIG. 12. Using the data in step 550 from the PLSanalysis, a decision whether to refine the matrix X is performed in step560. If refinement (i.e., reduction of the number of tool dataparameters to only significant tool data parameters) is required, thenthe procedure repeats the PLS analysis following step 570 with the newdata matrix X* in order to re-compute the corresponding new weighting,loading, variable influence, and score matrices. In step 570, thecriterion described in association with the VIP information presented inFIG. 12 are utilized to reduce the matrix X to a new matrix X*, wherethe reduced matrix has discarded those variables (columns) deemedunimportant for the process performance data (e.g., there is a weakcorrelation or minimal impact between the tool data parameter and theprocess performance data). Once step 560 determines that matrix X* isfinalized, step 580 is performed. Step 580 includes computing thecorrelation matrix B from equation (4) for later use as a processperformance prediction model. In step 590, the process performanceprediction model is incorporated with, for example, a fault detectionalgorithm.

Once the correlation matrix B has been evaluated (or the processperformance prediction model formulated), the correlation matrix B canbe used as part of a fault detection algorithm to provide robustdetermination and prediction of process faults. The fault detectionalgorithm can, in general, be applied to a variety of processes,however, the specific correlation matrix B developed as described abovewill be specific to a particular process in a specific process tool. Forexample, silicon processing, such as etching, can be performed in aprocess tool much like that depicted in FIGS. 1 through 5.

FIG. 17 presents a method of detecting a fault condition for a materialprocessing system employing a process performance prediction modelaccording to an embodiment of the present invention. The method includesa procedure 600 beginning with step 610 by preparing the chamberconditions for the specific process. For example, the chamber setupincludes loading the substrate to be processed, pumping down the vacuumchamber to a base pressure, initiating the flow of process gas, andadjusting the vacuum pump throttle valve to establish the chamberprocess pressure. In step 620, the plasma is ignited via, for example,the application of RF power to an electrode as discussed with referenceto FIGS. 2 through 5, thereby initiating the process. In step 630, anobservation of tool data is recorded. In step 640, the establishedprocess performance prediction model is used with the observed tool datato predict the process performance data, which includes projecting therecorded tool data onto the one or more correlation data (processperformance prediction model) via, for example, vector multiplication(or matrix multiplication). In step 650, the predicted processperformance data are compared with target process performance data. Thecomparison can include forming difference data from a numericaldifference, the square of a numerical difference, etc. In step 660, thedifference data is compared with threshold difference data, where afault is detected and/or predicted for the process when the differencedata exceeds the threshold difference data and, conversely, the processis operating within an acceptable range when the difference data doesnot exceed the threshold difference data. If the process is operatingwithin an acceptable range, then the process can continue in step 670.If a fault is detected or predicted, then an operator can be notified instep 680.

In the preceding text, methods of constructing a process performanceprediction model 110 and applying the model for fault detection havebeen described. Using the example from above, the detection of a faultcan subsequently be associated with either a fault in the mean trenchetch depth or the trench etch depth range (i.e., one of process rateand/or process uniformity).

For example, the occurrence of a fault in mean trench etch depth canarise due to perturbations to the process tool performance (e.g.,process drifts, etc.), sensor noise, drift in sensor calibration, etc.There exist a number of variables that are introduced from run-to-run,which can cause variations in process performance. However, with theformulation of a process performance prediction model 110, a predictionof process performance data, using an observation of tool data from aprior run, can be employed to update a process recipe in order tocorrect for process perturbations.

As described earlier, an element of the process recipe for (damascenestructure) trench etch is the trench etch time which requires knowledgeof the trench etch rate. According to one embodiment of the presentinvention, the process performance prediction model 110 can be utilizedto predict a mean trench etch depth from an observation of tool data.Dividing the predicted mean trench etch depth d_(predicted) by the etchtime τ_(old) during the prior run produces the predicted mean trenchetch rate ε_(predicted) (i.e., ε_(predicted)=d_(predicted)/ε_(old)).

Following the prediction of the mean trench etch rate using the processperformance prediction model 110 and the known etch time for the priorrun, a corrected etch time can be determined using the process recipecorrection filter 120. The process recipe correction filter 120 caninclude an exponentially weighted moving average (EWMA) filter tocorrect the etch time in the process recipe using the old value for theetch time, the predicted value for the etch time and a filter constant,viz.τ_(new)=(1−λ)τ_(old)+λ(τ_(predicted)−τ_(old)),  (5)where λ is the EWMA filter coefficient ( 0≦λ≦1), τ_(old) is the (old)process recipe etch time for the prior run, τ_(predicted) is thepredicted etch time using the known etch depth d for the (damascene)trench structure and the predicted etch rate ε_(predicted) (i.e.,τ_(predicted)=d/ε_(predicted)), and τ_(new) is the process recipe etchtime for the upcoming run. Note that when λ=0, the new etch time isequivalent to the old etch time and, when λ=1, the new etch time isequivalent to the predicted etch time.

FIG. 18 presents a determination of the optimal filter coefficient λ forthe prediction of the mean trench etch depth in order to minimize theroot mean square error (RMSE) and maximize the control algorithmstability. A series of fifty-two substrates were executed throughprocess tool 10, comprising four wet clean cycles. A first wet cleanpreceded substrate number one (i.e., first wet clean cycle comprisedsubstrates one to sixteen), a second wet clean preceded substrate numberseventeen (i.e., second wet clean cycle included substrates seventeen tothirty-six), a third wet clean preceded substrate number thirty-seven(i.e., third wet clean cycle comprised substrates thirty-seven toforty-seven), and a fourth wet clean preceded substrate numberforty-eight (i.e., fourth wet clean cycle comprised substratesforty-eight to fifty-two). During the execution of the fifty-twosubstrates, substrate numbers one through twenty-five were employed forthe development of a process performance prediction model 110, andsubstrate numbers twenty-six through fifty-two were employed for modelevaluation.

In general, when using substrate numbers twenty-six through fifty-twofor model evaluation, a RMSE of the measured and predicted data wasutilized to quantify the process performance prediction model 110performance, where the RMSE is defined as follows:

$\begin{matrix}{{RMSE} = {\sqrt{\frac{\sum\limits_{i = 1}^{n}\left( {ɛ_{predicted}^{i} - ɛ_{measured}^{i}} \right)^{2}}{\sum\limits_{i = 1}^{n}\left( {ɛ_{predicted}^{i} - {\overset{\_}{ɛ}}_{measured}} \right)^{2}}}.}} & (6)\end{matrix}$In equation (6), the summation over i=1 to n represents the summationover the substrates (twenty-six through fifty-two) used for modelevaluation, and ε _(measured) represents the average measured etch rate.Clearly, by inspection of FIG. 18, the optimal filter coefficient λ forthe aforesaid exemplary process is λ=0.5. According to the processrecipe correction filter 120, the process controller 130 can update theprocess recipe etch time prior to each substrate run.

FIG. 19 presents the measured trench etch rate (Angstroms (A)/minute(min)) and the predicted trench etch rate, using the process performanceprediction model 110 and the process recipe correction filter 120(λ=0.5), as a function of substrate number. It is evident by inspectionof FIG. 19 that the agreement between measured and predicted results isvery good with an average error of 26.5 Angstroms/minute (forapproximately a 5300 Angstroms/minute etch).

The average error can be further improved by implementing periodicupdates of the process performance prediction model 110. Atpre-determined intervals in substrate processing, a substrate can betransported from the process tool 10 to the metrology tool 140 formeasurement of the process performance data. Following the aboveexemplary process, the process performance data can include the meantrench etch depth and the trench etch range. This data can be measuredusing conventional techniques in metrology tool 140, as described above.

The measurement of process performance data can be submitted to theprocess performance model correction algorithm 150. Tool data and thecorresponding measured process performance data are inserted with thetool data matrix X and the process performance data matrix Y, and thecorrelation matrix B is re-evaluated using PLS analysis as describedwith reference to FIG. 16. The updated correlation matrix can then besubmitted to the process performance prediction model 110 forreplacement of the current process performance prediction model 110.FIG. 20 presents the data of FIG. 19 where the process performanceprediction model 110 is updated periodically using the processperformance correction algorithm 150. The average error is furtherreduced to 24.7 Angstroms/minute.

FIG. 21 presents a method of controlling a process in a materialprocessing system employing a process performance control systemaccording to an embodiment of the present invention. The method includesa procedure 700 beginning with step 710 where a designated process isexecuted. For example, process execution can include chamber setup(e.g., loading the substrate to be processed, pumping down the vacuumchamber to a base pressure, initiating the flow of process gas, andadjusting the vacuum pump throttle valve to establish the chamberprocess pressure), initiating the process (e.g., igniting plasma via,for example, the application of RF power to an electrode as discussedwith reference to FIGS. 2 through 5), and terminating a process (e.g.,completing a process according to a process time set by the processrecipe by terminating RF power, etc., and unloading a substrate). Duringthe process in step 710, an observation of tool data is recorded in step720. In step 730, a determination to update the process performanceprediction model is made. If necessary, the substrate processed duringthe process of step 710 is transported to the metrology tool (as isdescribed below with reference to steps 780 and 790). In step 740, theestablished process performance prediction model is used with theobserved tool data to predict the process performance data, whichincludes projecting the recorded tool data onto the one or morecorrelation data (process performance prediction model) via, forexample, vector multiplication (or matrix multiplication).

In step 750, the process recipe correction filter employs the EWMAfilter and model prediction of process performance data to correct theprocess recipe by determining a process recipe correction. Thecorrection to the process recipe can, for example, include a correctionto the etch time. The EWMA filter employs a filter coefficient and,desirably, the selected filter coefficient is the optimal filtercoefficient. In step 760, the process recipe is updated using theprocess controller prior to determining if another process is to be runin step 770.

If step 730 determines that the process performance prediction model isto be updated, then control passes to step 780. In step 780, processperformance data is measured using the metrology tool and submitted tothe process performance model correction algorithm. In step 790, theprocess performance model correction algorithm employs PLS analysisaccording to the procedures set forth above. The PLS analysis usesmeasured process performance data (from the metrology tool) andcorresponding tool data in addition to the tool data and processperformance data used for formulation of the current process performanceprediction model. When appropriate (as deemed by the length of time toupdate the process performance prediction model relative to executingsubstrate runs), the process performance prediction model is updated.

Although only certain exemplary embodiments of this invention have beendescribed in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention.

1. A material processing system comprising: a process tool; and aprocess performance control system configured to control said processtool, said process performance control system comprises a processperformance controller coupled to said process tool, wherein saidprocess performance controller comprises a process performanceprediction model configured to receive tool data from a plurality ofsensors associated with said process tool and predict processperformance data based on said tool data, a process recipe correctionfilter configured to receive predicted process performance data fromsaid process performance prediction model and determine a process recipecorrection based on said predicted process performance data and a knownvalue of the process recipe parameter to be corrected, said known valuebeing measured from a preceding process run, and a process controllerconfigured to update a process recipe for said process tool using saidprocess recipe correction.
 2. The material processing system as recitedin claim 1, wherein said process performance data comprises at least oneof etch depth, average etch depth, mean etch depth, and etch depthrange.
 3. The material processing system as recited in claim 1, whereinsaid tool data comprises at least one of a capacitor position, a forwardradio frequency (RF) power, a reflected RF power, a voltage, a current,a phase, an impedance, a RF peak-to-peak voltage, a RF self-induceddirect current bias, a chamber pressure, a gas flow rate, a temperature,a backside gas pressure, a backside gas flow rate, an electrostaticclamp voltage, an electrostatic clamp current, a focus ring thickness,RF hours, focus ring RF hours, and optical emission data.
 4. Thematerial processing system as recited in claim 1, wherein said processperformance prediction model comprises an output from a partial leastsquares analysis.
 5. The material processing system as recited in claim1, wherein said process recipe correction filter comprises anexponentially weighted moving average filter.
 6. The material processingsystem as recited in claim 1, wherein said process recipe correctioncomprises an etch time.
 7. The material processing system as recited inclaim 1, wherein said process performance control system furthercomprises a metrology tool and a process performance model correctionalgorithm.
 8. The material processing system as recited in claim 7,wherein said metrology tool is coupled to said process tool and saidprocess performance controller, and configured to measure processperformance data.
 9. The material processing system as recited in claim8, wherein said process performance model correction filter comprises apartial least squares algorithm that uses said measured processperformance data and corresponding tool data.
 10. A process performancecontrol system for controlling a process tool comprising: a processperformance controller capable of being coupled to said process tool,said process performance controller comprises a process performanceprediction model configured to receive tool data from a plurality ofsensors associated with said process tool and predict processperformance data based on said tool data, a process recipe correctionfilter configured to receive predicted process performance data fromsaid process performance prediction model and determine a process recipecorrection based on said predicted process performance data and a knownvalue of the process recipe parameter to be corrected, said known valuebeing measured from a preceding process run, and a process controllerconfigured to update a process recipe for said process tool using saidprocess recipe correction.
 11. The process performance control system asrecited in claim 10, wherein said process performance data comprises atleast one of mean etch depth and etch depth range.
 12. The processperformance control system as recited in claim 10, wherein said processperformance prediction model is configured to receive tool datacomprising at least one of a capacitor position, a forward RF power, areflected RF power, a voltage, a current, a phase, an impedance, a REpeak-to-peak voltage, a RF self-induced DC bias, a chamber pressure, agas flow rate, a temperature, a backside gas pressure, a backside gasflow rate, an electrostatic clamp voltage, an electrostatic clampcurrent, a focus ring thickness, RF hours, focus ring RF hours, andoptical emission data.
 13. The process performance control system asrecited in claim 10, wherein said process performance prediction modelcomprises an output from a partial least squares analysis.
 14. Theprocess performance control system as recited in claim 10, wherein saidprocess recipe correction filter comprises an exponentially weightedmoving average filter.
 15. The process performance control system asrecited in claim 10, wherein said process recipe correction comprises anetch time, a RF power, a pressure, a flow rate, a concentration, and atemperature.
 16. The process performance control system as recited inclaim 10, wherein said process performance control system furthercomprises a metrology tool capable of being coupled to said process tooland coupled to said process performance controller, and a processperformance model correction algorithm.
 17. The process performancecontrol system as recited in claim 16, wherein said metrology tool isconfigured to measure process performance data.
 18. The processperformance control system as recited in claim 17, wherein said processperformance model correction algorithm employs partial least squaresanalysis using said measured process performance data and correspondingtool data.
 19. A material process system comprising: a process tool; andmeans for controlling said process tool, said means for controllingcomprising means for predicting process performance data using tool datareceived from a plurality of sensors associated with said process tool,means for determining a process recipe correction using the predictedprocess performance data and a known value of the process recipeparameter to be corrected, the known value being measured from apreceding process run, and means for updating a process recipe for saidprocess tool using said process recipe correction.
 20. A method forcontrolling a process tool of a material processing system, the methodcomprising the steps of: executing a first process in said process toolusing a process recipe; recording tool data for said first process, saidtool data comprises a plurality of tool data parameters obtained from aplurality of sensors associated with the process tool; predictingprocess performance data from said tool data for said first processusing a process performance prediction model, said process performancedata comprises one or more process performance data parameters;determining a process recipe correction from said predicted processperformance data and a known value of the process recipe parameter to becorrected, the known value being measured from a preceding process runusing a process recipe correction filter; updating said process recipeby incorporating said process recipe correction using a processcontroller coupled to said process tool; and executing a second processin said process tool using said updated process recipe.
 21. The methodfor controlling a process tool as recited in claim 20, wherein saidmethod further comprises updating said process performance predictionmodel.
 22. The method for controlling a process tool as recited in claim21, wherein said updating said process performance prediction modelcomprises measuring process performance data for said first process insaid process tool, and determining a correction to said processperformance prediction model using said measured process performancedata for said first process, said recorded tool data for said firstprocess, and partial least squares analysis.
 23. The method forcontrolling a process tool as recited in claim 20, wherein said processrecipe comprises an etch time, a RF power, a pressure, a flow rate, aconcentration, and a temperature.
 24. The method for controlling aprocess tool as recited in claim 20, wherein said updated process recipecomprises an updated etch time.
 25. The method for controlling a processtool as recited in claim 20, wherein said process performance datacomprises at least one of mean etch depth and etch depth range.
 26. Themethod for controlling a process tool as recited in claim 20, whereinsaid tool data comprises at least one of a capacitor position, a forwardRE power, a reflected RF power, a voltage, a current, a phase, animpedance, a RE peak-to-peak voltage, a RE self-induced DC bias, achamber pressure, a gas flow rate, a temperature, a backside gaspressure, a backside gas flow rate, an electrostatic clamp voltage, anelectrostatic clamp current, a focus ring thickness, RE hours, focusring RE hours, and optical emission data.
 27. The method for controllinga process tool as recited in claim 20, wherein said process performanceprediction model comprises an output from a partial least squaresanalysis.
 28. The method for controlling a process tool as recited inclaim 20, wherein said determining a process recipe comprises using anexponentially weighted moving average filter.
 29. The method forcontrolling a process tool as recited in claim 20, wherein said methodfurther comprises: measuring process performance data using a metrologytool; and correcting said process performance prediction model usingsaid measured process performance data and said measured tool data. 30.The method for controlling a process tool as recited in claim 29,wherein said correcting said process performance prediction modelcomprises partial least squares analysis.
 31. The material processingsystem of claim 5, wherein said exponentially weighted moving averagefilter is configured to correct an etch time based on a measured etchtime of an immediately preceding etch process, a predicted value for theetch time and a filter constant.
 32. The material processing system ofclaim 14, wherein said exponentially weighted moving average filter isconfigured to correct an etch time based on a measured etch time of animmediately preceding etch process, a predicted value for the etch timeand a filter constant.
 33. The method of claim 28, wherein saiddetermining a process recipe comprises determining a corrected etch timebased on a measured etch time of an immediately preceding etch process,a predicted value for the etch time and a filter constant.